Laurent Cabaret

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—Optimizing connected component labeling is currently a very active research field. Some teams claim to have design the fastest algorithm ever designed. This paper presents a review of these algorithms and a enhanced benchmark that improve classical random images benchmark with a varying granularity set of random images in order to become closer to natural(More)
In the last decade, many papers have been published to present sequential connected component labeling (CCL) algorithms. As modern processors are multi-core and tend to many cores, designing a CCL algorithm should address parallelism and multithreading. After a review of sequential CCL algorithms and a study of their variations, this paper presents the(More)
The paper introduces the parallel version of the Light Speed Labeling (LSL) and compares it with the parallel versions of the competitors. A benchmark shows that the parallel Light Speed Labeling is ×1.8 faster than all the other algorithms for random images on average. This factor reaches ×3.2 for structured random images. More importantly, we show that(More)
This paper presents a new multi-pass <i>iterative</i> algorithm for Connected Component Labeling. The performance of this algorithm is compared to those of State-of-the-Art two-pass <i>direct</i> algorithms. We show that thanks to the parallelism of the SIMD multi-core processors and an activity matrix that avoids useless memory access, such algorithms have(More)
—Optimizing connected component labeling is currently a very active research field. The current most effective algorithms although close in their design are based on different memory/computation trade-offs. This paper presents a review of these algorithms and a detailed benchmark on several Intel and ARM embedded processors that allows to focus on their(More)
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